Visual object tracking is required in a variety of applications such as remote sensing, security, surveillance and monitoring, and military target detection. Typically, tracking of objects in video applications may be performed using blob, feature or contour based methods. Contour based methods provide details regarding boundaries of the object. However, such methods may require substantially high processing time for providing such information.
Certain applications employ blob based methods to track objects in video applications. In operation, blob based methods track the objects using geometric shapes such as a rectangle or an ellipse enclosing the object. One way of tracking objects using blobs is by using a mean shift tracker that models the object in different frames of a video application through histograms. Further, the position of the object is determined by comparing the histograms in different frames. Unfortunately, such trackers may not be able to handle scale and orientation changes of the object in the image frames.
Another way of tracking objects is by detection and trajectory estimation methods. However, such methods may be specific for particular objects and may require substantial offline training for generating a set of features for the objects. In addition, such techniques may require frequent updating of the set of features.